{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fea16e9c",
   "metadata": {},
   "source": [
    "### 6.3 K均值聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb8efc0",
   "metadata": {},
   "source": [
    "#### 6.3.3 对iris数据集进行K均值聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e6099fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbdfff3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载iris数据集\n",
    "iris = load_iris()\n",
    "data = iris.data\n",
    "target = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a49e097",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa2ff5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据各列的均值及标准差：\n",
      "\n",
      "      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "mean               5.84              3.06               3.76              1.20\n",
      "std                0.83              0.44               1.77              0.76\n",
      "标准化后各列的均值及标准差：\n",
      "\n",
      "      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "mean               -0.0              -0.0               -0.0              -0.0\n",
      "std                 1.0               1.0                1.0               1.0\n"
     ]
    }
   ],
   "source": [
    "print('原始数据各列的均值及标准差：\\n')\n",
    "print(np.round(pd.DataFrame(data,columns=iris.feature_names).describe().loc[['mean','std'],:],2))\n",
    "print('标准化后各列的均值及标准差：\\n')\n",
    "print(np.round(pd.DataFrame(data_scaled,columns=iris.feature_names).describe().loc[['mean','std'],:],2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13fc4e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, random_state=42)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建KMeans模型并拟合数据\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans.fit(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6a12065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 50, 0: 47, 2: 53})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取聚类结果\n",
    "labels = kmeans.labels_\n",
    "# 查看各类别的样本数量\n",
    "Counter(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45d5c5a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为了可视化，我们可以使用PCA将数据降维到二维\n",
    "pca = PCA(n_components=2)\n",
    "data_pca = pca.fit_transform(data_scaled)\n",
    "\n",
    "# 绘制聚类结果\n",
    "plt.scatter(data_pca[:, 0], data_pca[:, 1], c=labels, cmap='viridis', edgecolor='k')\n",
    "plt.title('K-Means Clustering of Iris Dataset')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03c67ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用肘部法确定最佳聚类簇数--簇内平方和\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets\n",
    "\n",
    "# 加载iris数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "\n",
    "# 尝试不同的聚类簇数\n",
    "k_values = range(1, 11)\n",
    "inertia_values = []\n",
    "\n",
    "for k in k_values:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X)\n",
    "    inertia_values.append(kmeans.inertia_)\n",
    "\n",
    "# 绘制肘部法图形\n",
    "plt.plot(k_values, inertia_values, marker='o')\n",
    "plt.title('Elbow Method for Optimal K')\n",
    "plt.xlabel('Number of Clusters (K)')\n",
    "plt.ylabel('Inertia (Within-Cluster Sum of Squares)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6901430a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn import datasets\n",
    "\n",
    "# 加载iris数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "\n",
    "# 尝试不同的聚类簇数\n",
    "k_values = range(2, 11)\n",
    "silhouette_scores = []\n",
    "\n",
    "for k in k_values:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(X)\n",
    "    labels = kmeans.labels_\n",
    "    silhouette_avg = silhouette_score(X, labels)\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "\n",
    "# 绘制轮廓系数图形\n",
    "plt.plot(k_values, silhouette_scores, marker='o')\n",
    "plt.title('Silhouette Coefficient for Optimal K')\n",
    "plt.xlabel('Number of Clusters (K)')\n",
    "plt.ylabel('Silhouette Coefficient')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cf748d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index (ARI): 0.7302382722834697\n"
     ]
    }
   ],
   "source": [
    "# 计算ARI值\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "from sklearn import datasets\n",
    "\n",
    "# 加载iris数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "true_labels = iris.target\n",
    "\n",
    "# 使用K均值聚类\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans.fit(X)\n",
    "predicted_labels = kmeans.labels_\n",
    "\n",
    "# 计算ARI\n",
    "ari = adjusted_rand_score(true_labels, predicted_labels)\n",
    "print(f\"Adjusted Rand Index (ARI): {ari}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf33aa0",
   "metadata": {},
   "source": [
    "### 6.4 层次聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6169105c",
   "metadata": {},
   "source": [
    "#### 6.4.3 对mtcars数据集进行层次聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22db5ff4",
   "metadata": {},
   "source": [
    "导入USArrests数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2496cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Murder  Assault  UrbanPop  Rape\n",
       "Alabama       13.2      236        58  21.2\n",
       "Alaska        10.0      263        48  44.5\n",
       "Arizona        8.1      294        80  31.0\n",
       "Arkansas       8.8      190        50  19.5\n",
       "California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "us_arrests = pd.read_csv('data/USArrests.csv',index_col =0) # 导入csv文件\n",
    "us_arrests.head() # 查看前五行"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b022b3",
   "metadata": {},
   "source": [
    "数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9765b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b34cd01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Murder  Assault  UrbanPop  Rape\n",
       "mean    -0.0      0.0      -0.0   0.0\n",
       "std      1.0      1.0       1.0   1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_arrests_scale = preprocessing.scale(us_arrests) # 数据标准化\n",
    "us_arrests_scale = pd.DataFrame(us_arrests_scale,index = us_arrests.index,\n",
    "                                columns = us_arrests.columns) # 转换为数据框\n",
    "np.round(us_arrests_scale.describe().loc[['mean','std'],:],1) # 查看标准化后的均值及标准差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7458056d",
   "metadata": {},
   "source": [
    "创建层次聚类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3fac49d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import matplotlib.pyplot as plt\n",
    "# 使用层次聚类\n",
    "# 假设我们分为3个簇\n",
    "n_clusters = 3\n",
    "clustering = AgglomerativeClustering(n_clusters=n_clusters).fit(us_arrests_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "81e9a837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 19, 1: 19, 2: 12})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(clustering.labels_) # 查看聚类结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4cafe89b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>1.255179</td>\n",
       "      <td>0.790787</td>\n",
       "      <td>-0.526195</td>\n",
       "      <td>-0.003451</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>0.513019</td>\n",
       "      <td>1.118060</td>\n",
       "      <td>-1.224067</td>\n",
       "      <td>2.509424</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>0.072361</td>\n",
       "      <td>1.493817</td>\n",
       "      <td>1.009122</td>\n",
       "      <td>1.053466</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>0.234708</td>\n",
       "      <td>0.233212</td>\n",
       "      <td>-1.084492</td>\n",
       "      <td>-0.186794</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.281093</td>\n",
       "      <td>1.275635</td>\n",
       "      <td>1.776781</td>\n",
       "      <td>2.088814</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Murder   Assault  UrbanPop      Rape  Cluster\n",
       "Alabama     1.255179  0.790787 -0.526195 -0.003451        0\n",
       "Alaska      0.513019  1.118060 -1.224067  2.509424        0\n",
       "Arizona     0.072361  1.493817  1.009122  1.053466        0\n",
       "Arkansas    0.234708  0.233212 -1.084492 -0.186794        1\n",
       "California  0.281093  1.275635  1.776781  2.088814        0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加聚类标签到数据框\n",
    "us_arrests_scale['Cluster'] = clustering.labels_\n",
    "\n",
    "# 输出带有聚类标签的数据框前五行\n",
    "us_arrests_scale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "148bef48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制树状图\n",
    "plt.figure(figsize=(16,6))\n",
    "linkage_matrix = linkage(us_arrests_scale.drop('Cluster', axis=1), 'ward')\n",
    "dendrogram(linkage_matrix, labels=us_arrests_scale.index, orientation='top')\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('Cluster Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "21d5ef14",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制叶子带州名称和聚类结果的树状图\n",
    "tree_labels = us_arrests_scale.index + \":\" + us_arrests_scale['Cluster'].astype(str)\n",
    "plt.figure(figsize=(16,6))\n",
    "linkage_matrix = linkage(us_arrests_scale.drop('Cluster', axis=1), 'ward')\n",
    "dendrogram(linkage_matrix, labels=tree_labels, orientation='top')\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('Cluster Distance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27cd5e0f",
   "metadata": {},
   "source": [
    "使用轮廓系数寻找最优簇数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "419a6ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "# 尝试不同的聚类簇数\n",
    "n_clusters = range(2, 11)\n",
    "silhouette_scores = []\n",
    "for k in n_clusters:\n",
    "    clustering = AgglomerativeClustering(n_clusters=k).fit(us_arrests_scale)\n",
    "    labels = clustering.labels_\n",
    "    silhouette_avg = silhouette_score(us_arrests_scale, labels)\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "# 绘制轮廓系数图形\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(n_clusters, silhouette_scores, marker='o')\n",
    "plt.title('Silhouette Coefficient for Optimal K')\n",
    "plt.xlabel('Number of Clusters (K)')\n",
    "plt.ylabel('Silhouette Coefficient')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2744595",
   "metadata": {},
   "source": [
    "### 6.5 密度聚类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33af9085",
   "metadata": {},
   "source": [
    "#### 6.5.3对multishapes数据集进行密度聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "969c9da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.803739</td>\n",
       "      <td>-0.853053</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.852851</td>\n",
       "      <td>0.367618</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.927180</td>\n",
       "      <td>-0.274902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.752626</td>\n",
       "      <td>-0.511565</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.706846</td>\n",
       "      <td>0.810679</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y  shape\n",
       "0 -0.803739 -0.853053      1\n",
       "1  0.852851  0.367618      1\n",
       "2  0.927180 -0.274902      1\n",
       "3 -0.752626 -0.511565      1\n",
       "4  0.706846  0.810679      1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# 导入数据集\n",
    "multishapes = pd.read_csv('data/multishapes.csv')\n",
    "multishapes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1c9e9132",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "sns.lmplot(data=multishapes,x='x',y='y',hue='shape',\n",
    "           markers=['o','d','x','s','+','*'],\n",
    "           scatter_kws={'s': 28},fit_reg=False)\n",
    "# 获取当前图形\n",
    "fig = plt.gcf()\n",
    "# 设置图形大小，可以根据需要调整宽度和高度\n",
    "fig.set_size_inches(16, 6)\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96e40df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 1100})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from collections import Counter\n",
    "\n",
    "# 提取 x 和 y 列\n",
    "X = multishapes[['x', 'y']]\n",
    "\n",
    "# 使用 DBSCAN 进行密度聚类\n",
    "dbscan = DBSCAN(eps=2, min_samples=2)\n",
    "multishapes['cluster'] = dbscan.fit_predict(X)\n",
    "\n",
    "# 查看聚类结果\n",
    "Counter(multishapes['cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6861f0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 410, -1: 31, 1: 405, 2: 104, 3: 99, 4: 51})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 寻找更优的DBSCAN聚类标签\n",
    "# 提取 x 和 y 列\n",
    "X = multishapes[['x', 'y']]\n",
    "# 使用 DBSCAN 进行密度聚类\n",
    "dbscan = DBSCAN(eps=0.15, min_samples=5)\n",
    "multishapes['cluster'] = dbscan.fit_predict(X)\n",
    "\n",
    "# 查看聚类结果\n",
    "Counter(multishapes['cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "db2d65f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图,用聚类标签作为散点颜色分组\n",
    "sns.lmplot(data=multishapes,x='x',y='y',hue='cluster',\n",
    "           markers=['o','d','x','s','+','*'],\n",
    "           scatter_kws={'s': 28},fit_reg=False)\n",
    "plt.rcParams['axes.unicode_minus'] = False  #用来正常显示负号\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "# 获取当前图形\n",
    "fig = plt.gcf()\n",
    "# 设置图形大小，可以根据需要调整宽度和高度\n",
    "fig.set_size_inches(16, 6)\n",
    "# 设置标题\n",
    "plt.title('DBSCAN聚类结果可视化')\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e02fd02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1334: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用K-Means对数据集进行聚类\n",
    "from sklearn.cluster import KMeans\n",
    "# 创建KMeans模型并拟合数据\n",
    "kmeans = KMeans(n_clusters=6, random_state=42)\n",
    "multishapes['cluster'] = kmeans.fit_predict(X)\n",
    "# 绘制散点图,用聚类标签作为散点颜色分组\n",
    "sns.lmplot(data=multishapes,x='x',y='y',hue='cluster',\n",
    "           markers=['o','d','x','s','+','*'],\n",
    "           scatter_kws={'s': 28},fit_reg=False)\n",
    "# 获取当前图形\n",
    "fig = plt.gcf()\n",
    "# 设置图形大小，可以根据需要调整宽度和高度\n",
    "fig.set_size_inches(16, 6)\n",
    "# 设置标题\n",
    "plt.title('K-Meas聚类结果可视化')\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
